R Implementation of Compressed Rule Ensemble Models introduced in "Compressed Rule Ensemble Learning) (2022) by Malte Nalenz and Thomas Augustin, accepted for publication at the AIStats 2022 conference.
library(devtools)
devtools::install_git("https://github.com/maltenlz/Compressed-Rule-Ensembles")
library(cre)
Draw data from mixture of normals:
x1 = c(rnorm(100,-1, 1), rnorm(100, 1, 1))
x2 = c(rnorm(100,-1, 1), rnorm(100, 1, 1))
x = cbind(x1, x2)
y = c(rep(1, times = 100), rep(0, times = 100))
Run the CRE model with default settings:
cre_mod = cre(x, y, task = "class")
Predict in-sample:
predict(cre_mod, x)
Look at the most important rules:
important_rules(cre_mod)
Also look the distribution of split points:
visualise_clusters(cre_mod)